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SETGENDATAREGION

SETGENDATAREGION sets out summary settlement data for generation within the specified region. The table is populated for participants using Data Model 5.2. For participants using Data Model 5.3 or higher, SET_ENERGY_REGION_SUMMARY replace SETGENDATAREGION.

Deprecation Warning

This table is in the HISTORICAL TABLES package, so is likely deprecated.

SETGENDATAREGION shows the regional summary. SETGENDATAREGION is public data.

Source

SETGENDATAREGION updates with each Settlement run.

Column Schema

PERIODID

Settlements Trading Interval.

SQL Type: NUMBER(22,10) Polars Type: pl.Float64

This column is mandatory, and a primary key.

Examples: 1, 200

REGIONID

Region Identifier

For more information, see the dedicated documentation page.

The SQL type is VARCHAR2(10). This is actually a factor/enum, with values:

  • QLD1

  • VIC1

  • TAS1

  • NSW1

  • SA1

  • SNOWY1

This column is mandatory, and a primary key.

SETTLEMENTDATE

Calendar Settlement Date

SQL Type: DATE Polars Type: pl.Date()

This column is mandatory, and a primary key.

Example: 2024/05/05 00:00:00

Note that AEMO represents dates as datetimes with a midnight time component.

VERSIONNO

Settlement run no

SQL Type: NUMBER(22,10) Polars Type: pl.Float64

This column is mandatory, and a primary key.

Example: 12

ENERGYCOST

Cost of net energy $

SQL Type: NUMBER(27,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: -269.95147, 1.59712, -18021.17474, 167606.21924

EXCESSENERGYCOST

Cost of excess energy $

SQL Type: NUMBER(27,5) Polars Type: pl.Float64

This column is not mandatory.

Example: 0

EXPENERGY

Export Energy (Generator Purchases)

SQL Type: NUMBER(27,6) Polars Type: pl.Float64

This column is not mandatory.

Examples: -0.212487, -49.58294

EXPENERGYCOST

Export Energy Cost

SQL Type: NUMBER(27,6) Polars Type: pl.Float64

This column is not mandatory.

Examples: -0.013823, 2.251715, -7037.281457, 2792.867124

LASTCHANGED

current system date, to enable automatic replication

For more information, see the dedicated documentation page.

SQL Type: DATE Polars Type: pl.Datetime()

This column is not mandatory.

Example: 2024/11/25 07:51:48

NETENERGY

Net energy MW/hours

SQL Type: NUMBER(27,5) Polars Type: pl.Float64

This column is not mandatory.

Examples: 52.65059, 636.02502

AENERGY

Purchased Energy - Not used in MMS Data Model

SQL Type: NUMBER(22,0) Polars Type: pl.Int128

This column is unused.

APOWER

Not used in MMS Data Model

SQL Type: NUMBER(22,0) Polars Type: pl.Int128

This column is unused.

GENERGY

Generated energy - Not used in MMS Data Model

SQL Type: NUMBER(22,0) Polars Type: pl.Int128

This column is unused.

GPOWER

Not used in MMS Data Model

SQL Type: NUMBER(22,0) Polars Type: pl.Int128

This column is unused.

New Data Timing

After each settlement run

Code Examples

Polars

Casting is enums, categoricals etc is not essential, but may speed up parsing. Swapping int for pl.UInt16, pl.Int32 would also speed up parsing. However some of the datatypes are inferred, so they may be wrong.

This code deduplicates for you. Deduplication is normally only required across multiple months.

This code assumes that all columns are present, which may not be true for older files.

import polars as pl
import polars.selectors as cs

# e.g. "2029/03/15 00:05:00"
dt_s_fmt = "%Y/%m/%d %H:%M:%S"

# Dates are represented as datetimes at midnight

d_fmt = "%Y/%m/%d 00:00:00"

csv_paths = ["SOMEFILE.CSV", "OTHERFILE.CSV"]

lf = (
    pl.concat(
        [
            pl.scan_csv(
                csv_path,  # must be unzipped beforehand
                skip_rows=1,  # skip the header
                infer_schema=False,  # much faster
            )
            .filter(cs.by_index(0) == "D")  # drop the footer
            .select(~cs.by_index(range(4)))  # drop metadata columns
            for csv_path in csv_paths
        ],
        how="diagonal_relaxed",
    )
    .with_columns(
        pl.col("LASTCHANGED").str.strptime(pl.Datetime, dt_s_fmt),
        pl.col("SETTLEMENTDATE").str.strptime(pl.Date, d_fmt),
    )
    .cast(
        {
            "PERIODID": pl.Float64,
            "REGIONID": pl.Enum(["QLD1", "VIC1", "TAS1", "NSW1", "SA1", "SNOWY1"]),
            "SETTLEMENTDATE": pl.Date(),
            "VERSIONNO": pl.Float64,
            "ENERGYCOST": pl.Float64,
            "EXCESSENERGYCOST": pl.Float64,
            "EXPENERGY": pl.Float64,
            "EXPENERGYCOST": pl.Float64,
            "NETENERGY": pl.Float64,
            "AENERGY": int,  # or pl.Int128
            "APOWER": int,  # or pl.Int128
            "GENERGY": int,  # or pl.Int128
            "GPOWER": int,  # or pl.Int128
        }
    )
    # Drop deprecated/unused columns
    .drop("AENERGY", "APOWER", "GENERGY", "GPOWER")
    # Deduplicate
    # We want one row per ['PERIODID', 'REGIONID', 'SETTLEMENTDATE', 'VERSIONNO']
    # Tie-breaker: greatest value of ['LASTCHANGED']
    .sort(["LASTCHANGED"], descending=False)
    .unique(subset=["PERIODID", "REGIONID", "SETTLEMENTDATE", "VERSIONNO"], keep="last")
)

Nemosis

This table is not yet available in Nemosis.

Example File

From PUBLIC_ARCHIVE_SETGENDATAREGION_FILE01_202412010000.zip

SETTLEMENTDATEVERSIONNOPERIODIDREGIONIDGENERGYAENERGYGPOWERAPOWERNETENERGYENERGYCOSTEXCESSENERGYCOSTEXPENERGYEXPENERGYCOSTLASTCHANGED
2024/05/05 00:00:00121NSW1598.7027549322.818530-3.611445-296.5842292024/11/25 07:51:48
2024/05/05 00:00:00121QLD1487.7835537936.493440-2.998403-233.9596042024/11/25 07:51:48
2024/05/05 00:00:00121SA1117.8916410009.684830-0.618345-53.682012024/11/25 07:51:48
2024/05/05 00:00:00121TAS1100.0442614679.758150-0.872541-123.0636972024/11/25 07:51:48
2024/05/05 00:00:00121VIC1413.5472834831.139020-43.378173-3691.5282642024/11/25 07:51:48
2024/05/05 00:00:00122NSW1594.9283478019.13560-3.544855-462.6580312024/11/25 07:51:48
2024/05/05 00:00:00122QLD1491.6136560482.122060-2.974245-367.0953782024/11/25 07:51:48
2024/05/05 00:00:00122SA1116.4609515787.44730-0.70283-97.1162982024/11/25 07:51:48
2024/05/05 00:00:00122TAS1100.0161514677.156340-0.916187-129.4599842024/11/25 07:51:48
2024/05/05 00:00:00122VIC1412.2769955129.324120-43.604794-5890.8966092024/11/25 07:51:48
2024/05/05 00:00:00123NSW1590.40676879.97150-3.522257-455.9515532024/11/25 07:51:48
...

CSETP.WORLDDVD_SETGENDATAREGIONAEMOPUBLIC2025/01/1015:05:52001736485552614MONTHLY_ARCHIVE001736485552614
ISETTLEMENTSGENDATAREGION5SETTLEMENTDATEVERSIONNOPERIODIDREGIONIDGENERGYAENERGYGPOWERAPOWERNETENERGYENERGYCOSTEXCESSENERGYCOSTEXPENERGYEXPENERGYCOSTLASTCHANGED
DSETTLEMENTSGENDATAREGION52024/05/05 00:00:00121NSW1598.7027549322.818530-3.611445-296.5842292024/11/25 07:51:48
DSETTLEMENTSGENDATAREGION52024/05/05 00:00:00121QLD1487.7835537936.493440-2.998403-233.9596042024/11/25 07:51:48
DSETTLEMENTSGENDATAREGION52024/05/05 00:00:00121SA1117.8916410009.684830-0.618345-53.682012024/11/25 07:51:48
DSETTLEMENTSGENDATAREGION52024/05/05 00:00:00121TAS1100.0442614679.758150-0.872541-123.0636972024/11/25 07:51:48
DSETTLEMENTSGENDATAREGION52024/05/05 00:00:00121VIC1413.5472834831.139020-43.378173-3691.5282642024/11/25 07:51:48
DSETTLEMENTSGENDATAREGION52024/05/05 00:00:00122NSW1594.9283478019.13560-3.544855-462.6580312024/11/25 07:51:48
DSETTLEMENTSGENDATAREGION52024/05/05 00:00:00122QLD1491.6136560482.122060-2.974245-367.0953782024/11/25 07:51:48
DSETTLEMENTSGENDATAREGION52024/05/05 00:00:00122SA1116.4609515787.44730-0.70283-97.1162982024/11/25 07:51:48
DSETTLEMENTSGENDATAREGION52024/05/05 00:00:00122TAS1100.0161514677.156340-0.916187-129.4599842024/11/25 07:51:48
DSETTLEMENTSGENDATAREGION52024/05/05 00:00:00122VIC1412.2769955129.324120-43.604794-5890.8966092024/11/25 07:51:48
DSETTLEMENTSGENDATAREGION52024/05/05 00:00:00123NSW1590.40676879.97150-3.522257-455.9515532024/11/25 07:51:48
...
CEND OF REPORT1003

SETTLEMENTDATE,VERSIONNO,PERIODID,REGIONID,GENERGY,AENERGY,GPOWER,APOWER,NETENERGY,ENERGYCOST,EXCESSENERGYCOST,EXPENERGY,EXPENERGYCOST,LASTCHANGED
"2024/05/05 00:00:00",12,1,NSW1,,,,,598.70275,49322.81853,0,-3.611445,-296.584229,"2024/11/25 07:51:48"
"2024/05/05 00:00:00",12,1,QLD1,,,,,487.78355,37936.49344,0,-2.998403,-233.959604,"2024/11/25 07:51:48"
"2024/05/05 00:00:00",12,1,SA1,,,,,117.89164,10009.68483,0,-0.618345,-53.68201,"2024/11/25 07:51:48"
"2024/05/05 00:00:00",12,1,TAS1,,,,,100.04426,14679.75815,0,-0.872541,-123.063697,"2024/11/25 07:51:48"
"2024/05/05 00:00:00",12,1,VIC1,,,,,413.54728,34831.13902,0,-43.378173,-3691.528264,"2024/11/25 07:51:48"
"2024/05/05 00:00:00",12,2,NSW1,,,,,594.92834,78019.1356,0,-3.544855,-462.658031,"2024/11/25 07:51:48"
"2024/05/05 00:00:00",12,2,QLD1,,,,,491.61365,60482.12206,0,-2.974245,-367.095378,"2024/11/25 07:51:48"
"2024/05/05 00:00:00",12,2,SA1,,,,,116.46095,15787.4473,0,-0.70283,-97.116298,"2024/11/25 07:51:48"
"2024/05/05 00:00:00",12,2,TAS1,,,,,100.01615,14677.15634,0,-0.916187,-129.459984,"2024/11/25 07:51:48"
"2024/05/05 00:00:00",12,2,VIC1,,,,,412.27699,55129.32412,0,-43.604794,-5890.896609,"2024/11/25 07:51:48"
"2024/05/05 00:00:00",12,3,NSW1,,,,,590.406,76879.9715,0,-3.522257,-455.951553,"2024/11/25 07:51:48"
...

C,SETP.WORLD,DVD_SETGENDATAREGION,AEMO,PUBLIC,2025/01/10,15:05:52,001736485552614,MONTHLY_ARCHIVE,001736485552614
I,SETTLEMENTS,GENDATAREGION,5,SETTLEMENTDATE,VERSIONNO,PERIODID,REGIONID,GENERGY,AENERGY,GPOWER,APOWER,NETENERGY,ENERGYCOST,EXCESSENERGYCOST,EXPENERGY,EXPENERGYCOST,LASTCHANGED
D,SETTLEMENTS,GENDATAREGION,5,"2024/05/05 00:00:00",12,1,NSW1,,,,,598.70275,49322.81853,0,-3.611445,-296.584229,"2024/11/25 07:51:48"
D,SETTLEMENTS,GENDATAREGION,5,"2024/05/05 00:00:00",12,1,QLD1,,,,,487.78355,37936.49344,0,-2.998403,-233.959604,"2024/11/25 07:51:48"
D,SETTLEMENTS,GENDATAREGION,5,"2024/05/05 00:00:00",12,1,SA1,,,,,117.89164,10009.68483,0,-0.618345,-53.68201,"2024/11/25 07:51:48"
D,SETTLEMENTS,GENDATAREGION,5,"2024/05/05 00:00:00",12,1,TAS1,,,,,100.04426,14679.75815,0,-0.872541,-123.063697,"2024/11/25 07:51:48"
D,SETTLEMENTS,GENDATAREGION,5,"2024/05/05 00:00:00",12,1,VIC1,,,,,413.54728,34831.13902,0,-43.378173,-3691.528264,"2024/11/25 07:51:48"
D,SETTLEMENTS,GENDATAREGION,5,"2024/05/05 00:00:00",12,2,NSW1,,,,,594.92834,78019.1356,0,-3.544855,-462.658031,"2024/11/25 07:51:48"
D,SETTLEMENTS,GENDATAREGION,5,"2024/05/05 00:00:00",12,2,QLD1,,,,,491.61365,60482.12206,0,-2.974245,-367.095378,"2024/11/25 07:51:48"
D,SETTLEMENTS,GENDATAREGION,5,"2024/05/05 00:00:00",12,2,SA1,,,,,116.46095,15787.4473,0,-0.70283,-97.116298,"2024/11/25 07:51:48"
D,SETTLEMENTS,GENDATAREGION,5,"2024/05/05 00:00:00",12,2,TAS1,,,,,100.01615,14677.15634,0,-0.916187,-129.459984,"2024/11/25 07:51:48"
D,SETTLEMENTS,GENDATAREGION,5,"2024/05/05 00:00:00",12,2,VIC1,,,,,412.27699,55129.32412,0,-43.604794,-5890.896609,"2024/11/25 07:51:48"
D,SETTLEMENTS,GENDATAREGION,5,"2024/05/05 00:00:00",12,3,NSW1,,,,,590.406,76879.9715,0,-3.522257,-455.951553,"2024/11/25 07:51:48"
...
C,"END OF REPORT",1003

Availability

Visibility: Public

This table is in the public monthly MMSDM archive.

I have collated the list of all those files into one place, in this CSV metadata file. (Columns year, month, url, compressed_size in bytes).

The earliest available data is month 1 of 2015. The most recent available data is month 12 of 2024.

Warning

There are some months with no files.

Data Size

Each year of data is approximately 13 MB when zipped, 131 MB as unzipped CSV.

Deduplication and PDR Loader Configuration

The primary keys are:

The transaction type is "insert", which means that you will usually not see duplicate data. If you do, you can deduplicate based on the primary keys, without sorting first. Rows from different files with the same primary keys should have the same values.

PDR Loader configuration is:

Report Name Report Subtype Version
SETTLEMENTS GENDATAREGION 5

This means that the CSV files start with metadata columns:

I,SETTLEMENTS,GENDATAREGION,5,...
D,SETTLEMENTS,GENDATAREGION,5,...

Indexes

This page as JSON (schema documented here, but may change)

Download a JSON Schema specification for this table.

Official AEMO schema documentation

Other Information

Package: HISTORICAL TABLES